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      name:  <unnamed>
       log:  C:\Users\cdj19\Desktop\Replication AJPS\Table 2 Estimates.log
  log type:  text
 opened on:  19 Jan 2016, 08:34:30

. do "C:\Users\cdj19\AppData\Local\Temp\STD01000000.tmp"

. ***** Data: Meritocracy Replication Data - Table 2 (for Table 2) *****
. 
. ** Model reported in paper: Logit model w/ random intercept **
. 
. xtmelogit divided ginicnty05_09_01 medhinc0610cnty_01 pctblk0610cnty_01 totpop0610cnty_01 pbush_01 ///
>         income_i_01  Age gender education_01 partyid_01 ideology_01 religattend_01 union unemployed ///
>         if white==1 || fips: ,

Refining starting values: 

Iteration 0:   log likelihood = -713.09954  
Iteration 1:   log likelihood =   -702.269  
Iteration 2:   log likelihood = -700.93607  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -700.93607  
Iteration 1:   log likelihood = -700.33354  
Iteration 2:   log likelihood = -700.30621  
Iteration 3:   log likelihood = -700.30613  
Iteration 4:   log likelihood = -700.30613  

Mixed-effects logistic regression               Number of obs      =      1119
Group variable: fips                            Number of groups   =       677

                                                Obs per group: min =         1
                                                               avg =       1.7
                                                               max =        19

Integration points =   7                        Wald chi2(14)      =     99.83
Log likelihood = -700.30613                     Prob > chi2        =    0.0000

------------------------------------------------------------------------------------
           divided |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
  ginicnty05_09_01 |    1.30659   .5840755     2.24   0.025     .1618234    2.451357
medhinc0610cnty_01 |   .1070615   .4901682     0.22   0.827    -.8536505    1.067774
 pctblk0610cnty_01 |  -.1940676    .464992    -0.42   0.676    -1.105435    .7172999
 totpop0610cnty_01 |   .6318651   .4837898     1.31   0.192    -.3163455    1.580076
          pbush_01 |   1.496315   .4939791     3.03   0.002      .528134    2.464497
       income_i_01 |  -.3648814   .2856864    -1.28   0.202    -.9248164    .1950535
               Age |   .0016944   .0044705     0.38   0.705    -.0070676    .0104564
            gender |  -.1144444   .1315061    -0.87   0.384    -.3721916    .1433029
      education_01 |   .4345028   .2841614     1.53   0.126    -.1224434     .991449
        partyid_01 |   -1.40967   .2138231    -6.59   0.000    -1.828756   -.9905848
       ideology_01 |  -.9093588   .3221883    -2.82   0.005    -1.540836   -.2778814
    religattend_01 |   .1642544   .2133255     0.77   0.441    -.2538559    .5823647
             union |   .3636423    .184286     1.97   0.048     .0024485    .7248362
        unemployed |   .1431809   .1560786     0.92   0.359    -.1627276    .4490894
             _cons |  -.7456005   .5308336    -1.40   0.160    -1.786015    .2948143
------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
fips: Identity               |
                   sd(_cons) |   1.00e-11   .2715586             0           .
------------------------------------------------------------------------------
LR test vs. logistic regression: chibar2(01) =     0.00 Prob>=chibar2 = 1.0000

. 
. ** Alternative specifications **
. 
. * Linear probability model w/ clustered ses *
. 
. reg divided ginicnty05_09_01 medhinc0610cnty_01 pctblk0610cnty_01 totpop0610cnty_01 pbush_01 ///
>         income_i_01  Age gender education_01 partyid_01 ideology_01 religattend_01 union unemployed if white==1, cluster(fips)

Linear regression                                      Number of obs =    1119
                                                       F( 14,   676) =    9.19
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0964
                                                       Root MSE      =  .47009

                                       (Std. Err. adjusted for 677 clusters in fips)
------------------------------------------------------------------------------------
                   |               Robust
           divided |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
  ginicnty05_09_01 |   .2911027   .1094125     2.66   0.008     .0762735    .5059319
medhinc0610cnty_01 |   .0280135   .0922642     0.30   0.762    -.1531454    .2091724
 pctblk0610cnty_01 |  -.0415678   .0949582    -0.44   0.662    -.2280163    .1448807
 totpop0610cnty_01 |   .1397641   .0608134     2.30   0.022     .0203582      .25917
          pbush_01 |   .3271743    .105562     3.10   0.002     .1199055    .5344431
       income_i_01 |  -.0795905   .0619498    -1.28   0.199    -.2012277    .0420468
               Age |   .0003707   .0009891     0.37   0.708    -.0015714    .0023127
            gender |  -.0255981   .0288005    -0.89   0.374    -.0821473     .030951
      education_01 |   .0960521   .0649773     1.48   0.140    -.0315295    .2236338
        partyid_01 |  -.3223839   .0486172    -6.63   0.000    -.4178427   -.2269251
       ideology_01 |  -.1982254   .0723262    -2.74   0.006    -.3402364   -.0562144
    religattend_01 |   .0389378   .0438764     0.89   0.375    -.0472127    .1250883
             union |   .0811322   .0386704     2.10   0.036     .0052036    .1570607
        unemployed |   .0308925   .0346444     0.89   0.373     -.037131     .098916
             _cons |   .3355745   .1105142     3.04   0.002     .1185822    .5525668
------------------------------------------------------------------------------------

. 
. * Logit model w/ clustered ses *
. 
. logit divided ginicnty05_09_01 medhinc0610cnty_01 pctblk0610cnty_01 totpop0610cnty_01 pbush_01 ///
>         income_i_01  Age gender education_01 partyid_01 ideology_01 religattend_01 union unemployed if white==1, cluster(fips)  

Iteration 0:   log pseudolikelihood = -755.99873  
Iteration 1:   log pseudolikelihood = -700.65662  
Iteration 2:   log pseudolikelihood = -700.30616  
Iteration 3:   log pseudolikelihood = -700.30613  

Logistic regression                               Number of obs   =       1119
                                                  Wald chi2(14)   =      98.14
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -700.30613                 Pseudo R2       =     0.0737

                                       (Std. Err. adjusted for 677 clusters in fips)
------------------------------------------------------------------------------------
                   |               Robust
           divided |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
  ginicnty05_09_01 |    1.30659   .5052868     2.59   0.010     .3162463    2.296934
medhinc0610cnty_01 |   .1070617   .4209779     0.25   0.799    -.7180399    .9321632
 pctblk0610cnty_01 |  -.1940676   .4416135    -0.44   0.660    -1.059614    .6714791
 totpop0610cnty_01 |   .6318648   .2721353     2.32   0.020     .0984895     1.16524
          pbush_01 |   1.496315   .4893733     3.06   0.002     .5371609    2.455469
       income_i_01 |  -.3648815   .2811503    -1.30   0.194     -.915926    .1861631
               Age |   .0016944   .0044717     0.38   0.705    -.0070699    .0104588
            gender |  -.1144443   .1312089    -0.87   0.383     -.371609    .1427204
      education_01 |   .4345027   .2979005     1.46   0.145    -.1493715    1.018377
        partyid_01 |   -1.40967   .2196879    -6.42   0.000     -1.84025   -.9790897
       ideology_01 |  -.9093587   .3273918    -2.78   0.005    -1.551035   -.2676826
    religattend_01 |   .1642544   .1987211     0.83   0.408    -.2252318    .5537405
             union |   .3636423   .1722143     2.11   0.035     .0261084    .7011762
        unemployed |   .1431809    .156418     0.92   0.360    -.1633927    .4497545
             _cons |  -.7456006   .4974008    -1.50   0.134    -1.720488     .229287
------------------------------------------------------------------------------------

.         
. * Linear probability model w/ random intercept *
. 
. xtmixed divided ginicnty05_09_01 medhinc0610cnty_01 pctblk0610cnty_01 totpop0610cnty_01 pbush_01 ///
>         income_i_01  Age gender education_01 partyid_01 ideology_01 religattend_01 union unemployed ///
>         if white==1 || fips: ,

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -746.46345  
Iteration 1:   log likelihood = -735.57703  
Iteration 2:   log likelihood = -735.57562  
Iteration 3:   log likelihood = -735.57562  

Computing standard errors:

Mixed-effects ML regression                     Number of obs      =      1119
Group variable: fips                            Number of groups   =       677

                                                Obs per group: min =         1
                                                               avg =       1.7
                                                               max =        19


                                                Wald chi2(14)      =    119.39
Log likelihood = -735.57562                     Prob > chi2        =    0.0000

------------------------------------------------------------------------------------
           divided |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------+----------------------------------------------------------------
  ginicnty05_09_01 |   .2911027   .1264755     2.30   0.021     .0432153    .5389901
medhinc0610cnty_01 |   .0280135   .1067681     0.26   0.793    -.1812481    .2372751
 pctblk0610cnty_01 |  -.0415678   .1001638    -0.41   0.678    -.2378853    .1547497
 totpop0610cnty_01 |   .1397641   .1067731     1.31   0.191    -.0695073    .3490354
          pbush_01 |   .3271743   .1066634     3.07   0.002     .1181178    .5362307
       income_i_01 |  -.0795905   .0624357    -1.27   0.202    -.2019622    .0427812
               Age |   .0003707   .0009784     0.38   0.705     -.001547    .0022884
            gender |  -.0255981   .0286494    -0.89   0.372    -.0817499    .0305536
      education_01 |   .0960521   .0617654     1.56   0.120    -.0250058      .21711
        partyid_01 |  -.3223839   .0467605    -6.89   0.000    -.4140328    -.230735
       ideology_01 |  -.1982254   .0703475    -2.82   0.005     -.336104   -.0603469
    religattend_01 |   .0389378   .0466426     0.83   0.404      -.05248    .1303556
             union |   .0811322   .0406142     2.00   0.046     .0015299    .1607345
        unemployed |   .0308925   .0341732     0.90   0.366    -.0360858    .0978708
             _cons |   .3355745    .116361     2.88   0.004     .1075111    .5636379
------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
fips: Identity               |
                   sd(_cons) |   4.30e-11   7.67e-08             0           .
-----------------------------+------------------------------------------------
                sd(Residual) |   .4669245     .00987      .4479749    .4866758
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) =     0.00 Prob >= chibar2 = 1.0000

. 
end of do-file

. log close
      name:  <unnamed>
       log:  C:\Users\cdj19\Desktop\Replication AJPS\Table 2 Estimates.log
  log type:  text
 closed on:  19 Jan 2016, 08:37:08
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